Chapter 4: Mining Frequent Patterns, Associations and Correlations

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29 Οκτ 2013 (πριν από 4 χρόνια και 11 μήνες)

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4.1 Basic Concepts

4.2 Frequent Itemset Mining Methods

4.3 Which Patterns Are Interesting?

Pattern Evaluation Methods

4.4 Summary

Chapter 4: Mining Frequent Patterns,
Associations and Correlations

Frequent Pattern Analysis

Frequent Pattern:

a pattern (a set of items, subsequences,
substructures, etc.) that occurs frequently in a data set

Goal:

finding inherent regularities in data

What products were often purchased together?

Beer and
diapers?!

What are the subsequent purchases after buying a PC?

What kinds of DNA are sensitive to this new drug?

Can we automatically classify Web documents?

Applications:

-
marketing, catalog design, sale
campaign analysis, Web log (click stream) analysis, and DNA
sequence analysis.

Why is Frequent Pattern Mining Important?

An important property of datasets

Foundation for many essential data mining tasks

Association, correlation, and causality analysis

Sequential, structural (e.g., sub
-
graph) patterns

Pattern analysis in spatiotemporal, multimedia, time
-
series, and
stream data

Classification: discriminative, frequent pattern analysis

Clustering analysis: frequent pattern
-
based clustering

Data warehousing: iceberg cube and cube
-

Semantic data compression

Frequent Patterns

itemset:

A set of one or more items

K
-
itemset

X = {x
1
, …, x
k
}

(absolute) support
, or,
support count

of X: Frequency or occurrence of an
itemset X

(relative)
support
,
s
, is the fraction of
transactions that contains X (i.e., the
probability

that a transaction
contains X)

An itemset X is
frequent
if X’s support
is no less than a
minsup

threshold

Customer

Customer

Customer

Tid

Items bought

10

Beer, Nuts, Diaper

20

Beer, Coffee, Diaper

30

Beer, Diaper, Eggs

40

Nuts, Eggs, Milk

50

Nuts, Coffee, Diaper, Eggs, Milk

Association Rules

Find all the rules
X

Y

with minimum
support and confidence

threshold

support

, s, probability that a

transaction contains X

Y

confidence
,
c, conditional
probability that a transaction having
X also contains Y

Let
minsup

= 50%,
minconf

= 80%

Freq. Pat.: Beer:3, Nuts:3, Diaper:4,
Eggs:3, {Beer, Diaper}:3

Association rules: (many more!)

Beer

Diaper (60%, 100%)

Diaper

Beer (60%, 75%)

Rules that satisfy both
minsup

and
minconf

are called
strong rules

Customer

Customer

Customer

Tid

Items bought

10

Beer, Nuts, Diaper

20

Beer, Coffee, Diaper

30

Beer, Diaper, Eggs

40

Nuts, Eggs, Milk

50

Nuts, Coffee, Diaper, Eggs, Milk

Closed Patterns and Max
-
Patterns

A long pattern contains a combinatorial number of sub
-
patterns,
e.g.,
{a
1
, …, a
100
}

contains

2
100

1
=
1.27
*
10
30
sub
-
patterns!

Solution:
Mine
closed patterns

and
max
-
patterns

An
itemset

X

is
closed

if X is
frequent

and there exists
no super
-
pattern

Y
כ

X,
with the same support

as X

An
itemset

X is a
max
-
pattern

if X is frequent and there exists no
frequent super
-
pattern Y
כ

X

Closed pattern is a lossless compression of freq. patterns

Reducing the number of patterns and rules

Closed Patterns and Max
-
Patterns

Example

DB = {<a
1
, …, a
100
>, < a
1
, …, a
50
>}

Min_sup
=1

What is the set of
closed
itemset
?

<a1, …, a100>: 1

< a1, …, a50>: 2

What is the set of
max
-
pattern
?

<a1, …, a100>: 1

What is the set of
all patterns
?

!!

Computational Complexity

How
many itemsets are potentially to be generated in the worst
case?

The number of frequent itemsets to be generated is sensitive to the
minsup threshold

When minsup is low, there exist potentially an exponential number of
frequent itemsets

The worst
case: MN where M: # distinct items, and N: max length
of transactions

4.1 Basic Concepts

4.2 Frequent Itemset Mining Methods

4.2.1
Apriori: A Candidate Generation
-
and
-
Test Approach

4.2.2
Improving the Efficiency of Apriori

4.2.3
FPGrowth: A Frequent Pattern
-
Growth Approach

4.2.4
ECLAT: Frequent Pattern Mining with Vertical Data Format

4.3 Which Patterns Are Interesting?

Pattern Evaluation Methods

4.4 Summary

Chapter
4
: Mining Frequent Patterns,
Associations and Correlations

4.2.1Apriori: Concepts and Principle

The
downward closure

property of frequent patterns

Any subset of a frequent itemset must be frequent

If {beer, diaper, nuts} is frequent, so is {beer, diaper}

i.e., every transaction having {beer, diaper, nuts} also contains
{beer, diaper}

Apriori pruning principle:

If there is

any

itemset which is infrequent,
its superset should not be generated/tested

4.2.1Apriori: Method

Initially, scan DB once to get frequent 1
-
itemset

Generate

length (k+1)
candidate
itemsets from length k frequent
itemsets

Test
the candidates against DB

Terminate when no frequent or candidate set can be generated

Apriori: Example

Database

1
st

scan

C
1

L
1

L
2

C
2

C
2

2
nd

scan

C
3

L
3

3
rd

scan

Tid

Items

10

A, C, D

20

B, C, E

30

A, B, C, E

40

B, E

Itemset

sup

{A}

2

{B}

3

{C}

3

{D}

1

{E}

3

Itemset

sup

{A}

2

{B}

3

{C}

3

{E}

3

Itemset

{A, B}

{A, C}

{A, E}

{B, C}

{B, E}

{C, E}

Itemset

sup

{A, B}

1

{A, C}

2

{A, E}

1

{B, C}

2

{B, E}

3

{C, E}

2

Itemset

sup

{A, C}

2

{B, C}

2

{B, E}

3

{C, E}

2

Itemset

{B, C, E}

Itemset

sup

{B, C, E}

2

Sup
min

=
2

Apriori Algorithm

C
k
: Candidate itemset of size k

L
k

: frequent itemset of size k

L
1

= {frequent items};

for

(
k

= 1;
L
k

!=

;
k
++)
do begin

C
k+1

= candidates generated from
L
k
;

for each

transaction
t

in database
do

increment the count of all candidates in C
k+1

that are
contained in t

L
k+1

= candidates in
C
k+1

with min_support

end

return

k

L
k
;

Candidate Generation

How to generate candidates?

Step 1: self
-
joining
L
k

Step 2: pruning

Example of Candidate Generation

L
3
={
abc
,
abd
,
acd
, ace,
bcd
}

Self
-
joining: L
3
*L
3
:
abcd

from
abc
,
abd
, and
bcd
,

acde

from
acd

and
ace

Pruning:
acde

is removed because

is not in
L
3

C4 = {
abcd
}

4.2.2 Generating Association Rules

Once the frequent itemsets have been found, it is straightforward
to generate
strong

association rules that satisfy:

minimum
support

minimum

confidence

Relation between support and confidence:

support_count(A

B
)

Confidence(A

B
) 㴠倨B籁)㴠

††††††††††††††††††††††††††††

Support_count(A

B

is the number of transactions containing the
itemsets A

B

Support_count(A)
is the number of transactions containing the
itemset A.

Generating Association Rules

For each frequent itemset
L
, generate all non empty subsets of
L

For
every non empty subset

S

of

L
,
output the rule
:

S

-

If (support_count(L)/support_count(S)) >= min_conf

Example

TID

List of item IDS

T100

I1,I2,I5

T200

I2,I4

T300

I2,I3

T400

I1,I2,I4

T500

I1,I3

T600

I2,I3

T700

I1,I3

T800

I1,I2,I3,I5

T900

I1,I2,I3

Suppose the frequent
Itemset

L={I1,I2,I5}

Subsets of L are:
{I1,I2},

{I1,I5},{I2,I5},{I1},{I2},{I5}

Association rules :

I1

I2

confidence = 2/4= 50%

I1

I5

confidence=2/2=100%

I2

I5

confidence=2/2=100%

I1

I2

I5

confidence=2/6=33%

I2

I1

I5

confidence=2/7=29%

I5

I2

I2

confidence=2/2=100%

If the minimum confidence =70%

Transactional Database

4.2.2
Improving the Efficiency of Apriori

Major computational challenges

Huge number of candidates

Multiple scans of transaction database

Tedious workload of support counting for candidates

Improving Apriori: general ideas

Shrink number of candidates

Reduce passes of transaction database scans

Facilitate support counting of candidates

(A) DHP: Hash
-
based Technique

Making a hash table

10: {A,C}, {A, D}, {C,D}
20: {B,C}, {B, E}, {C,E}

30: {A,B}, {A, C}, {A,E}, {B,C}, {B, E}, {C,E}

40: {B, E}

3

1

2

0

3

0

2

0

1

2

3

4

5

6

{C,E}

{C,E}

{A, B}

{A,E}

{B,C}

{B,C}

{B,E}

{B,E}

{B,E}

{A,C}

{A,C}

Hash codes

Buckets

Buckets

counters

min
-
support=2

We have the
following

binary vector

1 0 1 0 1 0 1

{A
,B}
1

{A, C}
2

{B,C}
2

{B, E}
3

{C,E}
2

{A, C}

{B,C}

{B, E}

{C,E}

J. Park, M. Chen, and P. Yu. An effective hash
-
based algorithm for mining association rules. SIGMOD’95

Database

1
st

scan

C
1

Tid

Items

10

A, C, D

20

B, C, E

30

A, B, C, E

40

B, E

Itemset

sup

{A}

2

{B}

3

{C}

3

{D}

1

{E}

3

(B) Partition: Scan Database Only Twice

Subdivide the transactions of
D

into

k

non overlapping partitions

Any itemset that is potentially frequent in
D

must be frequent in at
least one of the partitions
Di

Each partition can fit into main memory, thus it is read only once

Steps:

Scan1: partition database and find local frequent patterns

Scan2: consolidate global frequent patterns

A. Savasere, E. Omiecinski and S. Navathe, VLDB’95

D
1

D
2

D
k

+

= D

+

+

(C) Sampling for Frequent Patterns

Select a sample of the original database

Mine frequent patterns within the sample using Apriori

Use a lower support threshold than the minimum support to find
local frequent itemsets

Scan the database once to verify the frequent itemsets found in
the sample

Only broader frequent patterns are checked

Example: check abcd instead of ab, ac,…, etc.

Scan the database again to find missed frequent patterns

H. Toivonen. Sampling large databases for association rules. In VLDB’
96

(D) Dynamic: Reduce Number of Scans

S. Brin R. Motwani, J. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket
data. In SIGMOD’97

ABCD

ABC

ABD

ACD

BCD

AB

AC

BC

BD

CD

A

B

C

D

{}

Itemset lattice

Transactions

1
-
itemsets

2
-
itemsets

Apriori

1
-
itemsets

2
-
items

3
-
items

DIC

Once both A and D are determined
frequent, the counting of AD begins

Once all length
-
2 subsets of BCD are
determined frequent, the counting of
BCD begins

4.2.3 FP
-
growth: Frequent Pattern
-
Growth

Adopts a divide and conquer strategy

Compress the database representing frequent items into a
frequent

pattern tree

or
FP
-
tree

Retains the itemset association information

Divid the compressed database into a set of conditional
databases, each associated with one frequent item

Mine each such databases separately

Example: FP
-
growth

The first scan of data is the
same as Apriori

Derive the set of frequent 1
-
itemsets

Let min
-
sup=2

Generate a set of ordered
items

TID

List of item IDS

T100

I1,I2,I5

T200

I2,I4

T300

I2,I3

T400

I1,I2,I4

T500

I1,I3

T600

I2,I3

T700

I1,I3

T800

I1,I2,I3,I5

T900

I1,I2,I3

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

Construct the FP
-
Tree

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

-

Create a branch for each
transaction

-

Items in each transaction are
processed in order

1
-

Order the items T100: {I2,I1,I5}

2
-

Construct the first branch:

<I2:1>, <I1:1>,<I5:1>

TID

Items

TID

Items

TID

Items

T100

I1,I2,I5

T400

I1,I2,I4

T700

I1,I3

T200

I2,I4

T500

I1,I3

T800

I1,I2,I3,I5

T300

I2,I3

T600

I2,I3

T900

I1,I2,I3

I2:1

I1:1

I5:1

Construct the FP
-
Tree

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

-

Create a branch for each
transaction

-

Items in each transaction are
processed in order

1
-

Order the items T200: {I2,I4}

2
-

Construct the second branch:

<I2:1>, <I4:1>

TID

Items

TID

Items

TID

Items

T100

I1,I2,I5

T400

I1,I2,I4

T700

I1,I3

T200

I2,I4

T500

I1,I3

T800

I1,I2,I3,I5

T300

I2,I3

T600

I2,I3

T900

I1,I2,I3

I2:1

I1:1

I5:1

I4:1

I2:
2

Construct the FP
-
Tree

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

-

Create a branch for each
transaction

-

Items in each transaction are
processed in order

1
-

Order the items T300: {I2,I3}

2
-

Construct the third branch:

<I2:2>, <I3:1>

TID

Items

TID

Items

TID

Items

T100

I1,I2,I5

T400

I1,I2,I4

T700

I1,I3

T200

I2,I4

T500

I1,I3

T800

I1,I2,I3,I5

T300

I2,I3

T600

I2,I3

T900

I1,I2,I3

I2:2

I1:1

I5:1

I4:1

I3:1

I2:
3

Construct the FP
-
Tree

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

-

Create a branch for each
transaction

-

Items in each transaction are
processed in order

1
-

Order the items T400: {I2,I1,I4}

2
-

Construct the fourth branch:

<I2:3>, <I1:1>,<I4:1>

TID

Items

TID

Items

TID

Items

T100

I1,I2,I5

T400

I1,I2,I4

T700

I1,I3

T200

I2,I4

T500

I1,I3

T800

I1,I2,I3,I5

T300

I2,I3

T600

I2,I3

T900

I1,I2,I3

I1:1

I5:1

I4:1

I3:1

I2:3

I4:1

I1:
2

I2:
4

Construct the FP
-
Tree

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

-

Create a branch for each
transaction

-

Items in each transaction are
processed in order

1
-

Order the items T400: {I1,I3}

2
-

Construct the fifth branch:

<I1:1>, <I3:1>

TID

Items

TID

Items

TID

Items

T100

I1,I2,I5

T400

I1,I2,I4

T700

I1,I3

T200

I2,I4

T500

I1,I3

T800

I1,I2,I3,I5

T300

I2,I3

T600

I2,I3

T900

I1,I2,I3

I1:2

I5:1

I4:1

I3:1

I2:4

I4:1

I1:1

I3:1

Construct the FP
-
Tree

Transactional Database

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

TID

Items

TID

Items

TID

Items

T100

I1,I2,I5

T400

I1,I2,I4

T700

I1,I3

T200

I2,I4

T500

I1,I3

T800

I1,I2,I3,I5

T300

I2,I3

T600

I2,I3

T900

I1,I2,I3

I1:4

I5:1

I4:1

I3:2

I2:7

I4:1

I1:2

I3:2

I3:2

I5:1

When a branch of a
count for each node
along a common prefix is
incremented by 1

Construct the FP
-
Tree

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

I1:4

I5:1

I4:1

I3:2

I2:7

I4:1

I1:2

I3:2

I3:2

I5:1

The problem of mining frequent patterns in databases is
transformed to that of mining the FP
-
tree

Construct the FP
-
Tree

-
Occurrences of I5:

<I2,I1,I5> and <I2,I1,I3,I5>

-
Two prefix Paths

<I2, I1: 1> and <I2,I1,I3: 1>

-
Conditional FP tree contains only

<I2: 2, I1: 2>, I3 is not
considered because its support count of 1 is less than the
minimum support count.

-
Frequent patterns

{I2,I5:2}, {I1,I5:2},{I2,I1,I5:2}

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

I1:4

I5:1

I4:1

I3:2

I2:7

I4:1

I1:2

I3:2

I3:2

I5:1

Construct the FP
-
Tree

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

I1:4

I5:1

I4:1

I3:2

I2:7

I4:1

I1:2

I3:2

I3:2

I5:1

TID

Conditional Pattern Base

Conditional FP
-
tree

I5

{{I2,I1:
1
},{I2,I1,I3:
1
}}

<I2:
2
,I1:
2
>

I4

{{I2,I1:
1
},{I2,
1
}}

<I2:
2
>

I3

{{I2,I1:
2
},{I2:
2
}, {I1:
2
}}

<I2:
4
,I1:
2
>,<I1:
2
>

I1

{I2,
4
}

<I2:
4
>

Construct the FP
-
Tree

Item ID

Support
count

I2

7

I1

6

I3

6

I4

2

I5

2

null

I1:4

I5:1

I4:1

I3:2

I2:7

I4:1

I1:2

I3:2

I3:2

I5:1

TID

Conditional FP
-
tree

Frequent Patterns Generated

I5

<I2:
2
,I1:
2
>

{I2,I5:2}, {I1,I5:2},{I2,I1,I5:2}

I4

<I2:
2
>

{I2,I4:2}

I3

<I2:
4
,I1:
2
>,<I1:
2
>

{I2,I3:4},{I1,I3:4},{I2,I1,I3:2}

I1

<I2:
4
>

{I2,I1:4}

FP
-
growth properties

FP
-
growth transforms the problem of finding long frequent patterns
to searching for shorter once recursively and concatenating the
suffix

It uses the least frequent suffix offering a good selectivity

It reduces the search cost

If the tree does not fit into main memory, partition the database

Efficient and scalable for mining both long and short frequent
patterns

4.2.4 ECLAT: FP Mining with Vertical Data Format

Both
Apriori

and
FP
-
growth

use
horizontal data format

Alternatively data can also be represented in
vertical format

TID

List of item IDS

T100

I 1,I 2,I 5

T200

I 2,I 4

T300

I 2,I 3

T400

I 1,I 2,I 4

T500

I 1,I 3

T600

I 2,I 3

T700

I 1,I 3

T800

I 1,I 2,I 3,I 5

T900

I 1,I 2,I 3

itemset

TID_set

I 1

{T100,T400,T500,T700,T800,T900}

I 2

{T100,T200,T300,T400,T600,T800,T900}

I 3

{T300,T500,T600,T700,T800,T900}

I 4

{T200,T400}

I 5

{T100,T800}

ECLAT Algorithm by Example

Transform the horizontally formatted data to the vertical format
by scanning the database once

The support count of an itemset is simply the length of the TID_set
of the itemset

TID

List of item IDS

T100

I 1,I 2,I 5

T200

I 2,I 4

T300

I 2,I 3

T400

I 1,I 2,I 4

T500

I 1,I 3

T600

I 2,I 3

T700

I 1,I 3

T800

I 1,I 2,I 3,I 5

T900

I 1,I 2,I 3

itemset

TID_set

I 1

{T100,T400,T500,T700,T800,T900}

I 2

{T100,T200,T300,T400,T600,T800,T900}

I 3

{T300,T500,T600,T700,T800,T900}

I 4

{T200,T400}

I 5

{T100,T800}

ECLAT Algorithm by Example

The frequent k
-
itemsets can be used to construct the candidate
(k+1)
-
itemsets based on the Apriori property

itemset

TID_set

I 1

{T100,T400,T500,T700,T800,T900}

I 2

{T100,T200,T300,T400,T600,T800,T900}

I 3

{T300,T500,T600,T700,T800,T900}

I 4

{T200,T400}

I 5

{T100,T800}

Frequent 1
-
itemsets in vertical format

min_sup=2

Frequent 2
-
itemsets in vertical format

itemset

TID_set

{I 1,I2}

{T100,T400,T800,T900}

{I 1,I3}

{T500,T700,T800,T900}

{I 1,I4}

{T400}

{I 1,I5}

{T100,T800}

{I 2,I3}

{T300,T600,T800,T900}

{I 2,I4}

{T200,T400}

{I 2,I5}

{T100,T800}

{I 3,I5}

{T800}

ECLAT Algorithm by Example

This process repeats, with k incremented by 1 each time, until no
frequent items or no candidate itemsets can be found

Properties of mining with vertical data format

Take the advantage of the Apriori property in the generation of
candidate (k+1)
-
itemset from k
-
itemsets

No need to scan the database to find the support of (k+1)
itemsets, for k>=1

The TID_set of each k
-
itemset carries the complete information
required for counting such support

The TID
-
sets can be quite long, hence expensive to manipulate

Use
diffset

technique to optimize the support count computation

itemset

TID_set

{I 1,I2,I3}

{T800,T900}

{I 1,I2,I5}

{T100,T800}

Frequent 3
-
itemsets in vertical format

min_sup=2

4.1 Basic Concepts

4.2 Frequent Itemset Mining Methods

4.2.1
Apriori: A Candidate Generation
-
and
-
Test Approach

4.2.2
Improving the Efficiency of Apriori

4.2.3
FPGrowth: A Frequent Pattern
-
Growth Approach

4.2.4
ECLAT: Frequent Pattern Mining with Vertical Data Format

4.3 Which Patterns Are Interesting?

Pattern Evaluation Methods

4.4 Summary

Chapter 4: Mining Frequent Patterns,
Associations and Correlations

Strong Rules Are Not Necessarily Interesting

Whether a rule is interesting or not can be assesses either
subjectively or objectively

Objective interestingness measures can be used as one step
toward the goal of finding interesting rules for the user

Example of a misleading “strong” association rule

Analyze transactions of AllElectronics data about computer
games and videos

Of the
10,000
transactions analyzed

6,000
of the transactions include
computer games

7,500
of the transactions include
videos

4,000
of the transactions include
both

Suppose that min_sup=30% and min_confidence=60%

The following association rule is discovered:

“videos”
)[support =40%, confidence=66%]

Strong Rules Are Not Necessarily Interesting

“videos”
)[support 40%, confidence=66%]

This rule is strong but it is misleading

The probability of purshasing videos is
75%

which is even larger
than
66%

In fact computer games and videos are negatively associated
because the purchase of one of these items actually decreases
the likelihood of purchasing the other

The confidence of a rule
A

can be deceiving

It is only an estimate of the conditional probability of itemset
B
given itemset
A
.

It does not measure the real strength of the correlation implication
between
A
and
B

Need to use
Correlation Analysis

From Association to Correlation Analysis

Use
Lift
, a simple correlation measure

The occurrence of itemset
A

is independent of the occurrence of
itemset
B

if
P(A

B
)=P(䄩P(B)
, otherwise itemsets
A

and
B

are
dependent and correlated as events

The lift between the occurences of
A

and
B

is given by

Lift(A,B)=P(A

B
)/P(䄩P(B)

If > 1, then A and B are positively correlated (the occurrence of one
implies the occurrence of the other)

If <1, then A and B are negatively correlated

If =1, then A and B are independent

Example: P({game, video})=0.4/(0.60

0.75
)=0.89

4.1 Basic Concepts

4.2 Frequent Itemset Mining Methods

4.2.1
Apriori: A Candidate Generation
-
and
-
Test Approach

4.2.2
Improving the Efficiency of Apriori

4.2.3
FPGrowth: A Frequent Pattern
-
Growth Approach

4.2.4
ECLAT: Frequent Pattern Mining with Vertical Data Format

4.3 Which Patterns Are Interesting?

Pattern Evaluation Methods

4.4 Summary

Chapter 4: Mining Frequent Patterns,
Associations and Correlations

Basic Concepts:
association rules, support
-
confident framework,
closed and max patterns

Scalable frequent pattern mining methods

Apriori (Candidate generation & test)

Projection
-
based (FPgrowth)

Vertical format approach (ECLAT)

Interesting Patterns

Correlation analysis

4.4 Summary

Applications and Tools in Data Mining

1. Financial Data Analysis

Banks and Institutions offer a wise variety of banking services

Checking and savings accounts for business or individual
customers

Credit business, mortgage, and automobile loans

Investment services (mutual funds)

Insurance services and stock investment services

Financial data is relatively complete, reliable, and of high
quality

What to do with this data?

1. Financial Data Analysis

Design of data warehouses for multidimensional data analysis
and data mining

Construct
data warehouses
(data come from different sources)

Multidimensional Analysis
: e.g., view the revenue changes by
month. By region, by sector, etc. along with some statistical
information such as the mean, the average, the maximum and
the minimum values, etc.

Characterization and class comparison

Outlier analysis

1. Financial Data Analysis

Loan Payment Prediction and costumer credit policy analysis

Attribute selection and attribute relevance ranking may help
indentifying important factors and eliminate irrelevant ones

Example of factors related to the risk of loan payment

Term of the loan

Debt ratio

Payment to income ratio

Customer level income

Education level

Residence region

The bank can adjust its decisions

according to the subset of factors selected (use classification)

2. Retail Industry

Collect huge amount of data on sales, customer
shopping history, goods transportation,
consumption and service, etc.

Many stores have web sites where you can buy
online. Some of them exist only online (e.g.,
Amazon)

Data mining helps to

Discover customers shopping patterns and trends

Improve the quality of costumer service

Achieve better costumer satisfaction

Design more effective good transportation

2. Retail Industry

Design
data warehouses

Multidimensional
analysis

Analysis of the
effectiveness of sales campaigns

Comparing transactions that contain sales items
during and after the campaign

Costumer retention

Analyze the change in costumers behaviors

Product
Recommendation

Mining association rules

Display associative information to promote sales

3. Telecommunication Industry

Many different ways of communicating

Fax, cellular phone, Internet messenger, images, e
-
mail, computer and Web data transmission, etc.

Great demand of data mining to help

Indentifying telecommunication patterns

Catching fraudulent activities

Making better use of resources

Improve the quality of service

3. Telecommunication Industry

Multidimensional
analysis (several attributes)

Several features: Calling time, Duration, Location of
caller, Location of callee, Type of call, etc.

Compare data traffic, system workload, resource
usage, user group behavior, and profit

Fraudulent Pattern Analysis

Identify potential fraudulent users

Detect attempts to gain fraudulent entry to costumer
accounts

Discover unusual patterns (outlier analysis)

4. Many Other Applications

Biological
Data Analysis

E.g., identification and analysis of human genomes
and other species

Web
Mining

E.g., explore linkage between web pages to compute
authority scores (Page Rank Algorithm)

Intrusion detection

Detect any action that threaten file integrity,
confidentiality, or availability of a network resource

How to Choose a Data Mining System (Tool)?

Do data mining systems share the same well defined operations
and a standard query language?

No

Many commercial data mining systems have a little in common

Different functionalities

Different methodology

Different data sets

You need to carefully choose the data mining system that is

How to Choose a Data Mining System (Tool)?

Data Types and Sources

Available systems handle formatted record
-
based, relational
-
like
data with numerical, and nominal attributes

That data could be on the form of ASCII text, relational databases,
or data warehouse data

It is important to check which kind of data the system you are
choosing can handle

It is important that the data mining system supports ODBC
connections (Open Database Connectivity)

Operating System

A data mining system may run only on one operating system

The most popular operating systems that host data mining tools
are UNIX/LINUX and Microsoft Windows

How to Choose a Data Mining System (Tool)?

Data Mining functions and Methodologies

Some systems provide only one data mining function(e.g.,
classification). Other system support many functions

For a given data mining function (e.g., classification), some
systems support only one method. Other systems may support
many methods (k
-
nearest neighbor, naive Bayesian, etc.)

Data mining system should provide default settings for non experts

How to Choose a Data Mining System (Tool)?

Coupling data mining with databases(data warehouse) systems

No Coupling

A DM system will not use any function of a DB/DW system

Fetch data from particular resource (file)

Process data and then store results in a file

Loose coupling

A DM system use some facilities of a DB/DW system

Fetch data from data repositories managed by a DB/DW

Store results in a file or in the DB/DW

Semi
-
tight coupling

Efficient implementation of few essential data mining primitives
(sorting, indexing, histogram analysis) is provided by the DB/DW

Tight coupling

A DM system is smoothly integrated into the DB/DW

Data mining queries are optimized

Tight coupling is highly desirable because it facilitates
implementations and provide high system performance

How to Choose a Data Mining System (Tool)?

Scalability

Query execution time should increase linearly with the number of
dimensions

Visualization

“A picture is worth a thousand words”

The quality and the flexibility of visualization tools may strongly
influence usability, interpretability and attractiveness of the system

Data Mining Query Language and Graphical user Interface

High quality user interface

It is not common to have a query language in a DM system

Examples of Commercial Data Mining Tools

Database system and graphics vendors

Intelligent Miner (IBM)

Microsoft SQL Server 2005

MineSet (Purple Insight)

Oracle Data Mining (ODM)

Examples of Commercial Data Mining Tools

Vendors of statistical analysis or data mining software

Clementine (SPSS)

Enterprise Miner (SAS Institute)

Insightful Miner (Insightful Inc.)

Examples of Commercial Data Mining Tools

Machine learning community

CART (Salford Systems)

See5 and C5.0 (RuleQuest)

Weka developed at the university Waikato (open source)

End of The Data Mining Course

Questions? Suggestions?